The Real Life of a Data Scientist

The biggest difficulty in constructing a model is understanding the relevance of each data set to a given analysis task. When data scientists get to this stage, they often find their data has not been completely transformed and must go back to the wrangling stage in order to identify useful patterns or relationships. Data scientists also find that during this stage, many existing analytics packages, tools or algorithms do not scale with the size of their data sets.

In early 2012, a group of Stanford University researchers interviewed 35 data analysts from 25 organizations across a variety of sectors, including health care, retail, marketing and finance, and identified the various challenges data scientists face in the data analysis process.

Despite being in high demand and hailed as one of the hottest professions of the 21st century, much of the work of a data scientist is actually dominated by the incredibly time-consuming process of changing data into a usable form. The data analysis process involves four tasks - discovery, transformation, modeling and reporting – with data scientists spending as much as 60 to 80 percent of their time in the data transformation stage.

In this slideshow, Trifacta, a provider of productivity platforms for data analysis, takes you through each of these tasks in greater detail, highlighting the pain points data scientists face at each stage. It’s clear tools are needed that can simplify the data analysis process while at the same time increasing productivity and collaboration among data scientists.

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